DBSCAN-based energy consumption pattern clustering identification method for 5G base-station

Abstract

To fully understand the energy consumption characteristics of 5G base-station, a DBSCAN-based energy consumption pattern clustering identification method is proposed for 5G base-station. Firstly, this paper analyzes the daily-curve characteristics of power consumption behavior in typical application scenarios of 5G base-station for further pattern clustering identification. Then, the proposed pattern clustering identification method is depicted based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering decision, which is composed of the feature extraction for power consumption daily-curve of 5G base-station. Finally, the experiment is implemented using actual operation data of 5G base-station as data source. The experiment results illustrate that the proposed method can effectively identify the clustering characteristics of the energy consumption behavior for 5G base-station.